深度挖掘数据潜在价值提高找矿靶区定量优选精度

Deeply mining the intrinsic value of geodata to improve the accuracy of predicting by quantitatively optimizing method for prospecting target areas

找矿效果取决于找矿靶区预测的准确程度,传统的“综合信息成矿预测”(定性研究)已无法深入挖掘现有地质信息的潜在价值。本文应用大数据思想、方法,对甘肃省祁连山—龙首山地区1∶20万区域化探数据做分幅平差,消除了原始数据的系统误差。应用回归分析建立信息修复模型,增强了化探信息与区域Cu矿的相关关系。通过判别分析算法,构建了区域“化探信息Cu找矿靶区定量优选系列模型”,对研究区Cu找矿靶区做出定量预测。经统计,一、二级预测靶区中包含已知铜矿的比率高于22.0%,其面积仅占总研究区的1.72%。大数据找矿靶区定量预测在大幅提高预测精度的同时,很大程度地缩小了预测找矿靶区的面积。在对系列模型预测效果做出定量评价的同时,通过所建系列模型组合元素的特征分析,该研究也为进一步研究区域矿床成因和控矿条件提供了定量依据。

Ore prospecting relies on accurate target prediction.The traditional information-based qualitative metallogenic prognostic method,however,has not been able to perform geodata deep mining.In this paper,Big Data deep mining methodology and leveling technique were applied to 1∶200000 scale stream sediment geochemical survey data collected from the Qilian and Longshou Mountains region,Gansu Province to eliminate systematic errors in the raw data.Through regression analysis an information repair model was established to improve the correlation between survey results and copper ore deposits.Using discriminant analysis algorithm,a series of quantitatively optimized prediction models for copper ore prospecting were developed.These prediction models quantitatively predicted copper prospecting target areas.According to the statistical analysis,the proportion of known copper ore deposits exceeded 22%of the class 1 or class 2 predicted target areas,while the target areas only covered 1.72%of the total studied area.Thus,quantitative prediction of prospecting target area using Big Data has greatly increased prediction accuracy while markedly reduced prediction acreage.By characteristic analysis of model elements,the predictive power of the prediction model series was quantitatively evaluated,which provided a basis for the quantitative evaluation of ore genesis and regional ore control conditions.